CN108429574B - Method for selecting transmitting antenna of large-scale MIMO system - Google Patents
Method for selecting transmitting antenna of large-scale MIMO system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0452—Multi-user MIMO systems
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
- H04B7/0691—Hybrid systems, i.e. switching and simultaneous transmission using subgroups of transmit antennas
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- H—ELECTRICITY
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- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0602—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using antenna switching
- H04B7/0608—Antenna selection according to transmission parameters
- H04B7/061—Antenna selection according to transmission parameters using feedback from receiving side
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The invention discloses a method for selecting transmitting antennas of a large-scale MIMO system, which is based on a transmitting antenna selection algorithm for maximizing the total power criterion of a user received signal so as to improve the error rate performance of the system, and uses a convex optimization method to obtain the optimal solution, and comprises the following specific steps: (1) initializing; (2) antenna selection; (3) precoded transmissions that maximize signal-to-leakage-and-noise ratio; (4) The receiving of each user receiver effectively improves the feasibility of solving by using the convex optimization method and reduces the operation amount. The method for selecting the transmitting antenna of the large-scale MIMO system has the characteristics of high feasibility, low operation amount, convenience in operation and the like.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a method for selecting transmitting antennas of a large-scale MIMO system.
Background
The massive MIMO system, which may also be referred to as a massive antenna array, has antennas with more orders of magnitude than the conventional MIMO system, and the massive MIMO system can significantly improve the performance such as link reliability, data rate, and spectral efficiency, but in practical applications, due to the physical size limitation and implementation complexity, the number of antennas used at the base station end cannot be infinite, so the antenna selection technology of the base station becomes a research hotspot.
The optimal algorithm for antenna selection is a traversal search method, i.e., all combinations of N selected from M antennas are traversed, and an optimal solution meeting a certain criterion is calculated. Such a poor search method is hardly feasible with a huge calculation amount when the number of antennas is large. In a conventional MIMO system with a small number of antennas, an iterative algorithm or a poor search method is used to select an antenna combination with the largest channel transmission matrix norm, but these methods have too many computation amounts to be feasible when the number of transmit/receive antennas is large. In the massive MIMO system, a method for selecting an antenna based on the maximized channel capacity is provided, and a relaxation method is used for solving the convex optimization problem, so that the operation complexity is reduced. There are also methods for selecting antennas based on the criterion of maximizing the eigenvalues of the minimum channel matrix for improving the system error rate performance. These methods all use relaxation methods, which yield solutions that are only sub-optimal with better performance. For example, in the proposed method of maximizing system and capacity, when selecting a smaller number of antennas among a very large number of antennas, the capacity loss due to relaxation solution is close to 5%.
Disclosure of Invention
The present invention is directed to solving, at least in part, one of the technical problems in the related art. Therefore, an object of the present invention is to provide a transmit antenna selection algorithm based on a criterion of maximizing total power of received signals of users to improve the error rate performance of the system, and to use a convex optimization method to obtain an optimal solution, thereby effectively improving the feasibility of solving by using the convex optimization method and reducing the computation workload.
The invention discloses a method for selecting transmitting antennas of a large-scale MIMO system, which is characterized in that the transmitting system of the large-scale MIMO system comprises a base station comprising a large-scale antenna array, an MIMO channel and a single-antenna user receiver, wherein the number of the antennas at a transmitting end is 100-1000, a receiving end is a plurality of single-antenna users, the number of the selected antennas is adjustable, the MIMO channel is a Rayleigh flat fading channel, user data is BPSK (binary phase Shift keying) signals with unit power, the base station knows CSI (channel state information), and each precoding vector is ensured by 2 norms thereof, and the method comprises the following specific steps:
1. the method for selecting the transmitting antenna of the massive MIMO system comprises the following steps:
(1) Initialization
(1a) The base station transmitting terminal numbers all transmitting antennas in sequence;
(1b) Constructing a channel transmission matrix from all transmitting antennas of the base station to a single receiving antenna of each user according to the known channel state information;
(2) Antenna selection
(2a) Solving a channel selection matrix according to a criterion of maximizing a Frobenius norm of a channel transmission matrix;
(2b) Solving the adopted transmitting antenna number by using a channel selection matrix;
(2c) Obtaining a channel transmission matrix corresponding to the selected channel according to the channel number of the selected channel;
(3) Precoding transmission to maximize signal-to-leakage-and-noise ratio
(3a) Solving the precoding matrix of each user according to the selected channel transmission matrix;
(3b) Performing linear preprocessing on information to be sent by a base station by using a precoding matrix, namely, pre-multiplying the precoding matrix by an information vector;
(3c) Modulating the preprocessed information by using the selected antenna and then sending out the information;
(4) Receiving procedure of each user receiver
(4a) Solving a receiving processing matrix by a method of matching filtering of each user;
(4b) And the received signal is multiplied by the receiving processing matrix for the left side and then demodulated, useful information is recovered, and one-time communication is completed.
The beneficial effects of the invention are as follows: the method for selecting the transmitting antenna of the massive MIMO system is based on the maximization of the receiving power of all users. Simulation results show that the algorithm has high feasibility, and has better BER performance and larger capacity than the original random selection scheme. Compared with the maximum sum capacity algorithm, the error rate performance of the system is improved although the sum capacity is reduced. And the solutions obtained by the proposed algorithm through convex optimization calculation are all optimal solutions, so that the calculation complexity is obviously reduced, and the calculation efficiency is improved. The algorithm can be used in large-scale array antennas, and an effective solution is provided for antenna selection of a large-scale MIMO system.
Drawings
FIG. 1 is a basic workflow of the system of the present invention;
FIG. 2 is a basic block diagram of a large-scale antenna system of the present invention;
FIG. 3 is a graph of bit error rate performance for the antenna selection algorithm of the present invention;
fig. 4 is a graph of the sum capacity of the antenna selection algorithm of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "transverse," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, but are not intended to indicate or imply that the device or element so referred to must have a particular orientation, be constructed and operated in a particular orientation, and are not to be construed as limiting the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be interconnected within two elements or in a relationship where two elements interact with each other unless otherwise specifically limited. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "above," and "over" a second feature may be directly on or obliquely above the second feature, or simply mean that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
As shown in fig. 2, the base station in the downlink of the system has M transmit antennas for communicating with K single-antenna users. The CSI is completely known by a sending end of a down link base station of the MIMO system, and the best N (K < N < M) antennas are selected from M antennas to serve K users to form the large-scale MIMO system according to a certain criterion.
The input-output relationship of a MIMO system can be described in the following vector form:
y＝Hs+n
wherein y = [ y ] 1 y 2 y 3 ...y K ]Represents a K × 1 received signal vector; s = [ s ] 1 s 2 s 3 ...s m ]A signal vector representing a transmitting end; n = [ n = 1 n 2 n 3 ...n K ]Is a K × 1 complex Gaussian random vector with a variance of
H denotes a channel matrix representing the size K × M from the transmitting end to the user.
Referring to fig. 1, the steps implemented by the present invention are as follows:
The base station transmitting end numbers all transmitting antennas in sequence; and constructing a channel transmission matrix H from all transmitting antennas of the base station to a single receiving antenna of each all users according to the known channel state information.
Solving a channel selection matrix according to a criterion of maximizing the Frobenius norm square of a channel transmission matrix; solving the adopted transmitting antenna number by using the channel selection matrix; and obtaining the corresponding channel transmission matrix according to the channel number of the selected channel.
Introducing an M × M diagonal matrix gamma with diagonal elements of gamma i (i = 1.... M), which is a binary variable indicating whether the ith antenna is driven or notAnd (4) selecting. At the same time must satisfy
To meet the total transmit power constraint. By selecting the matrix γ, our optimization objective is:
max γ tr(H * γH)
subject γ i ∈{0，1}
for massive MIMO systems where M may exceed 100, it is difficult to search for an ergodic path because of the very large number of possible antenna combinations. Convex optimization methods solve the above problem, and it is noted that not a convex problem, but other constraints are convex problems. So we have constraints in the above equation:
subject to 0≤γ i ≤1
the objective problem thus becomes a convex optimization problem that can be solved. This problem can be solved with an interior point algorithm. This treatment makes gamma i The N maximum values are chosen from the scores, the subscripts of which represent the antennas chosen at the base station side. The simulation result shows that the optimal solution gamma in the above formula i The result of the software CVX, which yields only 1 or 0, not a score, shows that the proposed selection scheme is better than the existing solvability and is simpler and more feasible.
Step 3, precoding transmission of maximized signal-to-leakage-to-noise ratio
Solving the precoding matrix of each user according to the selected channel transmission matrix; performing linear preprocessing on information to be sent by a base station by using a precoding matrix, namely, pre-multiplying the precoding matrix by an information vector; modulating the preprocessed information by using the selected antenna and then sending out the information;
the present invention uses the maximum SLNR precoding method to transmit data to verify the performance of the proposed antenna selection algorithm. As defined therein, the SLNR of the kth user is:
wherein p is k Is a vector representing a 1 × N precoding for the kth user. h is Nk Is a channel representing a size of 1 × N from the base station to the kth user. Here, the optimal channel selected from the k users is referred to. The optimal precoding matrix for the kth user is a matrix pair
And &>
The maximum generalized eigenvalue of (2) corresponds to the eigenvector. Using a precoding matrix p k The information vector s of the user is then pre-multiplied and transmitted using the selected antenna.
Each user adopts zero forcing or minimum mean square error method to solve receiving processing matrix, namely the preprocessing matrix is G k ＝(h k p k ) + /||(h k p k ) L; and the received signal is multiplied by the receiving processing matrix for the left side and then demodulated, useful information is recovered, and one-time communication is completed.
The effect of the invention can be further illustrated by the following simulation experiment conditions:
based on the above analysis, the algorithm for maximizing the total power of the users proposed herein is much better than the random antenna selection algorithm in capacity performance, but lower than the maximization and capacity algorithm; the error rate performance is obviously superior to the random selection algorithm and is slightly better than the maximization and capacity algorithm; solvability, our algorithmOptimal solution gamma from convex optimization i Only 1 or 0, rather than a fraction, the solvability is higher. The algorithm provided by the invention can obtain the global optimal solution of the target problem, and the maximization and capacity algorithm obtains the local optimal solution, so that the algorithm is simpler and more feasible; in terms of operation complexity, assuming that the number of antennas at the transmitting end of the large-scale MIMO system is M, the number of receiving ends is K single-antenna users, and the Monte-Carlo simulation times is N, the calculation amount of the maximization and capacity algorithm is about [ (M-1) M 2 +M 2 +M！]X (M × K × N) addition operation and [ M 3 +M！(M-1)]X (M × K × N) multiplication operations. The algorithm is proposed to operate with an amount of about [ (M-1) M 2 ]X (M × K × N) times of addition and M 3 X (M × K × N) multiplication operations. The reduction of the sum of the volumes (M) compared to the maximization and capacity algorithm 2 + M! ) X (M X K X N) times of addition and M! (M-1) × (M × K × N) multiplication operations. In a large-scale MIMO system, M is more than or equal to 100 and less than or equal to 1000, so the calculation complexity of the proposed algorithm is smaller.
Simulation results and discussion
To analyze the performance of the proposed algorithm, monte-Carlo simulations were performed on the proposed algorithm, maximization and capacity algorithm, and random selection antenna algorithm, comparing their system capacity and bit error rate performance. In the simulation, it is assumed that the number of antennas at the downlink transmitting end is 128, the receiving end is a user with 3 single antennas, the MIMO channel is a Rayleigh flat fading channel, and the user data is a BPSK signal with unit power. The CSI is known to the base station and each precoding vector is guaranteed | p by its 2-norm k | 2 ＝1。
To compare the bit error rate performance of several algorithms, figure 3 shows BER curves for three algorithms. From simulation results, the bit error rate performance of the proposed algorithm is slightly higher than the BER performance of the maximization and capacity algorithm, but is obviously better than the BER performance of the random antenna selection algorithm. It can obtain more diversity gain of large-scale MIMO system.
Fig. 4 shows the sum capacity performance comparison of three algorithms, where 2000 independent channel matrices are used for the calculation. For each channel, the channel model is subject to flat rayleigh fading. As can be seen from the simulation results, the system and capacity of the proposed scheme is higher than the random antenna selection scheme and lower than the maximum and capacity scheme. It can be seen from the figure that the number K of users is not changed, and the system and capacity of the scheme are greatly improved along with the increase of the number N of selected antennas. It can be seen that the scheme herein is designed from the viewpoint of improving error performance, but still maintains a larger multiplexing gain of the massive MIMO system.
In the present invention, we propose an effective antenna selection algorithm for the transmitting end of a massive MIMO system, which is based on maximizing the total received power of all users. Simulation results show that the algorithm has high feasibility, and has better BER performance and larger capacity than the original random selection scheme. Compared with the maximum sum capacity algorithm, the error rate performance of the system is improved although the sum capacity is reduced. And the solutions obtained by the proposed algorithm through convex optimization calculation are all optimal solutions, so that the calculation complexity is obviously reduced, and the calculation efficiency is improved. The algorithm can be used in large-scale array antennas, and an effective solution is provided for antenna selection of a large-scale MIMO system.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although the embodiments of the present invention have been shown and described, it is understood that the above embodiments are illustrative and not restrictive, and that those skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.
Claims (1)
1. The method for selecting the transmitting antenna of the large-scale MIMO system is characterized in that the transmitting system of the large-scale MIMO system comprises a base station of a large-scale antenna array, an MIMO channel and a single-antenna user receiver, wherein the number of the antennas at a transmitting end is 100-1000, a receiving end is a plurality of single-antenna users, the number of the selected antennas is adjustable, the MIMO channel is a Rayleigh flat fading channel, user data is BPSK (binary phase Shift keying) signals of unit power, CSI (channel state information) is known by the base station, and each precoding vector ensures | p by 2 norms of the precoding vector k | 2 =1, the method for selecting the transmitting antenna of the massive MIMO system comprises the following steps:
(1) Initialization
(1a) The transmitting end of the base station numbers all transmitting antennas in sequence and records h ik ；
(1b) Constructing a channel transmission matrix H from all transmitting antennas of the base station to a single receiving antenna of each user according to the known channel state information;
(2) Antenna selection
(2a) Solving a channel selection matrix according to a square criterion of the Frobenius norm of the maximized channel transmission matrix;
(2b) The number of the adopted transmitting antenna is solved by utilizing the channel selection matrix, N antennas are selected from M antennas for transmitting, and the solving method by using convex optimization comprises the following steps: firstly, introducing an M × M diagonal matrix gamma with diagonal elements of gamma i (i = 1.. Multidot.m), which is a binary variable indicating whether the ith antenna is selected; at the same time, must satisfy
To meet a total transmit power constraint; by selecting the matrix γ, our optimization objective is:
max γ tr(H * γH)
subject γ i ∈{0，1}
for a massive MIMO system where M may exceed 100, it is difficult to perform a search for traversal because of the large number of possible antenna combinations; the convex optimization method is used for solving the problems, and the convex problem is not noticed, and other limiting conditions are convex problems; so we have constraints in the above equation:
subject to 0≤γ i ≤1
thus, the target problem becomes a convex optimization problem which can be solved; this problem can be solved with an interior point algorithm; the treatment makes gamma i Changing into a fraction, and selecting N maximum values from the fraction, wherein subscripts of the N maximum values represent the antennas selected at the base station side; the simulation result shows that the optimal solution gamma in the above formula i The result of the software CVX is only 1 or 0, not a score, and the result shows that the selection scheme proposed by the software CVX is better than the existing solvability, and is simpler and more feasible;
(2c) Obtaining the corresponding channel transmission matrix according to the channel number of the selected channel, namely according to the gamma with the value of 1 i Selecting a transmitting antenna for the subscript of (4);
(3) Precoding transmission to maximize signal-to-leakage-and-noise ratio
(3a) Solving the precoding matrix of each user according to the selected channel transmission matrix, wherein the precoding matrix of each user is realized according to the following formula:
wherein p is k Is a 1 × N precoding vector representing the kth userAn amount; h is a total of Nk Is a channel representing a size of 1 × N from the base station to the kth user; here, the optimal channel selected from k users is referred to; the optimal precoding matrix for the kth user is a matrix pair +>
And &>
The feature vector corresponding to the maximum generalized feature value of (1); using a precoding matrix p k The information vector s of the user is multiplied and then is sent out by using the selected antenna;
(3b) Performing linear preprocessing on information to be sent by a base station by using a precoding matrix, namely, pre-multiplying the precoding matrix by an information vector;
(3c) Modulating the preprocessed information by using the selected antenna and then sending out the information;
(4) Receiving procedure of each user receiver
(4a) Each user adopts a matched filtering method to solve a receiving processing matrix;
(4b) And the received signal is multiplied by the receiving processing matrix for the left, and then demodulation is carried out to recover useful information, thereby completing one-time communication.
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